Micheal Lanham

Micheal Lanham is a distinguished software and technology innovator with more than two decades of experience in the industry. He has an extensive background in developing various software applications across several domains, such as gaming, graphics, web development, desktop engineering, AI, GIS, oil and gas geoscience/geomechanics, and machine learning. Micheal began by pioneering work in integrating neural networks and evolutionary algorithms into game development, which began around the turn of the millennium. He has authored multiple influential books exploring deep learning, game development, and augmented reality, including Evolutionary Deep Learning (Manning, 2023) and Augmented Reality Game Development (Packt Publishing, 2017). He has contributed to the tech community via publications with many significant tech publishers, including Manning. Micheal resides in Calgary, Alberta, Canada, with his large family, whom he enjoys cooking for.

books by Micheal Lanham

AI Agents in Action
  • February 2025
  • ISBN 9781633436343
  • 344 pages
Included with a Manning Online subscription
  • printed in black & white

In AI Agents in Action, you’ll learn how to build production-ready assistants, multi-agent systems, and behavioral agents. You’ll master the essential parts of an agent, including retrieval-augmented knowledge and memory, while you create multi-agent applications that can use software tools, plan tasks autonomously, and learn from experience. As you explore the many interesting examples, you’ll work with state-of-the-art tools like OpenAI Assistants API, GPT Nexus, LangChain, Prompt Flow, AutoGen, and CrewAI.

Evolutionary Deep Learning
  • June 2023
  • ISBN 9781617299520
  • 360 pages
Included with a Manning Online subscription
  • printed in black & white
  • Available translations: Simplified Chinese

Evolutionary Deep Learning introduces evolutionary computation (EC) and gives you a toolbox of techniques you can apply throughout the deep learning pipeline. Discover genetic algorithms and EC approaches to network topology, generative modeling, reinforcement learning, and more! Interactive Colab notebooks give you an opportunity to experiment as you explore.